An embedding converts an input into a numeric vector whose position represents learned relationships. Semantic search embeds a query, compares it with stored vectors, and returns nearby items. Proximity can find related meaning despite different wording, but it does not prove relevance, truth, authority, permission, or currentness.
Who this is for: Application developers and data-minded product teams evaluating semantic search for documents, support, or recommendations.
- Use embeddings for candidate discovery, then apply metadata, ranking, and business constraints.
- Judge search with labeled user questions and expected results rather than attractive demonstrations.
- Treat model choice, vector dimensions, and API details as current documentation questions.
Vectors represent learned relationships
An embedding model maps text or another supported input into an ordered list of numbers. Inputs used in similar contexts tend to produce vectors that are nearer by a chosen distance measure. This makes it possible to match 'reset my login' with documentation titled 'recover account access' even when few words overlap.
The individual numbers are not useful labels that a person reads one by one. Meaning comes from their combined position and the model that produced them. Vectors from different embedding models are generally not interchangeable. If the embedding model changes, plan to re-embed the relevant corpus and compare retrieval before switching production traffic.
Similarity creates candidates
At indexing time, the system embeds each searchable unit and stores its vector with source metadata. At query time, it embeds the question and asks for nearby vectors. A similarity score orders candidates, but the numerical value has meaning only within the tested setup. Avoid declaring a universal threshold without labeled examples from the application.
Pure vector similarity can return conceptually related but operationally wrong material. A query about canceling a current plan may retrieve an old cancellation policy or a document for another region. Apply access, product, locale, date, and document-state filters. Hybrid search can combine lexical matches with vectors when exact codes, names, or phrases matter.
Design the searchable unit
The text embedded as one unit affects what can be found. A whole manual may blur distinct topics, while a single sentence may lack the heading that gives it meaning. Preserve titles and nearby context where useful. Store the original text separately so the search result can be inspected and cited rather than reconstructed from a vector.
Queries also need care. Very short input can be ambiguous, and long requests may combine several intents. The application can add known context, split subquestions, or ask for clarification. Do not quietly add assumptions that change the user's need. Log privacy-safe query categories and failed searches so improvements respond to actual use.
Evaluate relevance and coverage
Build a set of real or representative questions with one or more acceptable results. Measure whether useful items appear near the top, whether important queries return nothing, and whether restricted or obsolete documents appear. Review by query class because a good average can hide poor performance on rare but consequential requests.
Compare semantic, keyword, and hybrid methods against the same set. Inspect false positives to decide whether the cause is chunk content, missing metadata, broad queries, or the embedding representation. Provider models and limits evolve, so verify current dimensions, input constraints, and recommended usage in official documentation during quarterly review.
Improve search for a help center
Users search 'money taken twice,' but the relevant article is titled 'Resolve a duplicate card charge.'
- Create searchable units from article sections and attach product, region, publication state, and canonical URL metadata.
- Embed the units and the user query with the same current embedding model, then retrieve a small candidate set.
- Filter unpublished and wrong-region articles, combine semantic score with exact matches for card terminology, and rank the remainder.
- Record whether the duplicate-charge article appears near the top across paraphrases such as billed twice and repeated payment.
- Add confusing results to the labeled test set before changing chunk text, filters, or ranking weights.
Semantic search evaluation sheet
Use this compact record for each representative information need.
- Query and intent: exact wording, user context, and the need being judged.
- Expected results: acceptable source identifiers and why each is relevant.
- Forbidden results: restricted, obsolete, or misleading candidates that must not surface.
- Observed ranking: positions from keyword, semantic, and hybrid methods.
- Diagnosis: embedding, chunk, metadata, query, or ranking change to test next.
Common mistakes
- Treating vector similarity as evidence that a result is authoritative or factually correct.
- Mixing vectors created by different models inside one index without a migration and comparison plan.
- Evaluating search with a few hand-picked queries whose wording closely matches the desired documents.
Try one
A semantic search for 'close my account' returns articles about closing browser tabs and closing support tickets. Propose a grounded fix.
The response should inspect the query context, document metadata, and labeled intent before changing a threshold. Useful changes may filter to account-help content, combine exact account terms with semantic candidates, or ask which account when several products exist. A strong answer adds this query and paraphrases to an evaluation set and checks that the fix does not suppress valid close-ticket searches in their own context.
Sources
- OpenAI embeddings guideOfficial explanation of embeddings and common semantic search uses.
- OpenAI retrieval guideOfficial guidance for indexing, searching, and retrieving relevant source material.